Apple execs are now acknowledging the company is going to need to raises prices to cope with higher component costs, particularly memory. Apple has been battling to avoid doing so, but warned this week that those efforts are “unsustainable.”

The price pressure is being felt across the tech industry. Omdia predicts the average selling price (ASP) of smartphones globally will increase by around 20% this year — partly because AI industry demands have pushed up RAM prices and partly because of rapid cost increases in processor production as a direct consequence of war in the Middle East. This impacts anything that uses memory or processors.

Apple must raise prices, CEO warns

“We’re doing our best to mitigate the huge increases that are being passed to us, and we’ve been trying to shield our customers from the increases, but the situation has become unsustainable,” current Apple CEO Tim Cook told the Wall Street Journal.

OK, so prices are going to go up. But how?

If Apple must raise prices, the way it does so matters. Will it increase prices across the board, or choose a subtle, strategic approach that protects margins while also maintaining rapidly accelerating market share growth?

Until now, Apple has been benefiting from the rising tide in memory prices. While competitors have been steadily increasing prices across the board, Apple introduced its first $599 laptop even as it eats into mid-range smartphone sales. 

To grow, or to grow less?

The PC industry is not growing (outside of Cupertino), and Apple and its investors already know that the inordinately high customer satisfaction ratings its platforms enjoy mean that when people switch to them they tend to stick around. That’s why it matters that Cook highlighted record switchers and first-time platform users during the company’s most recent fiscal call. Apple is growing new customers, people who are likely to sign-up for services, accessories, and future upgrades. Winning the customer is an investment in future performance.

When it comes to increasing prices, we’ve seen some gentle experimentation in the Mac mini line, where the entry-level $599 model was effectively replaced by a more expensive $799 SKU. There have been suggestions Apple could do the same with the $599 MacBook Neo, dumping the entry-level build in favor of the (better) $699 model. I’m not certain that’s the best approach. 

A matter of choice

I’d argue that the $599 price point is psychologically the best bit of marketing Apple has ever done as it draws new users to look at its platforms. I also think substantial numbers of people already shift up to the $699 version, if they can, making that entry-level price an incredibly effective marketing tool with which to woo customers. It looks like that approach is working; the MacBook Neo has led the Amazon Laptop Sales chart ever since it was introduced and is selling in huge quantities globally.

Francisco Jeronimo, IDC vice president for data and analytics, sees this, writing: “It will be interesting to see whether Apple raises prices across its entire portfolio or plays strategically by subsidizing its more affordable products to gain market share and grow its installed base.”

If Apple raises prices at the price sensitive, entry-level end of its market, it runs the risk of slowing its own resurgence. Given that a 10% price hike here is only worth around $60, the revenue generated might not justify blunting Apple’s offer to new users.

Pricing and strategy

But Apple isn’t defined by its entry-level markets. Its biggest earning opportunities continue to be generated by the aspirational middle class, who, while feeling the pinch of declining living standards in many markets, can still handle the price of a top-of-the-range iPhone Pro. A 10% price increase at that end of the market will generate a much more significant chunk of revenue – and since rival vendors are raising their prices at this end of the market, Apple can follow suit while still seeming affordable. It’s an approach that maintains growth while optimizing margins.

Apple generates roughly half its revenue from iPhone sales, with the Pro and Pro Max devices accounting for a significant chunk of that money. Consumers looking to buy one of Apple’s high-end devices are likely to be less price-sensitive than those making purchases at the low- and mid-points of the company’s market range. 

With that in mind, it makes more sense for the company to raise prices at the top, while working to maintain the cost of entry at the lower end. It could, of course, raise prices universally, but doing so makes less sense.

What next?

Whatever strategy Apple adopts, company leadership is clear that component pricing is hitting hard. “We definitely need memory pricing and supply to return to reasonable levels for consumer products. That’s the bottom line,” Cook said.

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A federal judge has rebuffed Workday’s claim that it cannot be held liable under California anti-discrimination laws when its tools are used to screen (and potentially reject) job candidates in other states.

This week, US District Judge Rita Lin indicated that she will likely allow additional state discrimination claims against Workday to move forward. This would significantly expand the closely-watched case and likely ratchet up scrutiny of AI recruiting tools and their potentially inherent biases when it comes to age, race, sex, disabilities, and other factors.

Further, it could indicate that, even if a company is not the final employer, it may be held liable if its tools materially influence who gets rejected. This could set new legal standards for AI hiring systems, and have implications across industries, experts note.

“This case reinforces the importance of actually managing AI risks,” said Valence Howden, advisory fellow at Info-Tech Research Group. “If an AI-enabled model or ATS [Applicant Tracking System] is making decisions based on historical information, it can raise questions about whether bias in outcomes and datasets has been properly addressed.”

The case so far

Mobley v. Workday, Inc. alleges that Workday’s AI screening tools discriminate against job seekers based on age, race, and disability. The suit was filed in 2024 in the US District Court of California by Derek Mobley, a Black disabled man over 40, who claimed Workday’s algorithms continually screened him out as he applied for more than 100 positions on the platform.

The claims alleged discrimination prohibited by several US and California statutes: Race and sex under the Civil Rights Act of 1964 (Title VII); disability under the Americans with Disabilities Act of 1990 (ADA); age under the Age Discrimination in Employment Act of 1967 (ADEA); and race, gender, and age under California Fair Employment and Housing Act (FEHA).

Specifically, the suit centered around Workday’s use of automated, algorithm-driven tools for applicant screening. It alleged that these systems rely on historical data and statistical modeling that can make them susceptible to existing biases, even if protected characteristics like race, age, sex, or disability are not explicitly provided.

Bias may enter these systems in different ways, the plaintiffs argued, including via training data, model design, and evaluation criteria for candidate fit. The system could reproduce discriminatory outcomes by making correlations from data. For instance, years of experience on a resumé may indicate age; long employment gaps may infer a disability or caregiving responsibilities; educational and institutional affiliations could reflect race.

Workday has argued that it is not subject to liability under employment statutes because it does not qualify as the job applicants’ “employer.” But federal judges have allowed key parts of the lawsuit to move forward, ruling that Workday could potentially be treated as an employer’s “agent” for the purposes of anti-discrimination law.

The latest dispute centers on FEHA. According to legal sources, the California statute is among the strongest anti-discrimination laws in the US, in many cases providing broader protections than federal employment laws.

Workday asked the court to dismiss claims brought under California law, saying FEHA should not apply to the hiring decisions of out-of-state employers and applicants. The company’s lawyers argued that enforcing this would effectively allow California law to supersede that of other states, just because a company used their platform.

But Lin disagreed, saying FEHA does apply, and in fact, Workday is directly liable for its “own engagement in FEHA-regulated activities on the employer’s behalf.” Holding businesses liable for “their own discriminatory conduct” is within the scope and purposes of FEHA and consistent with public policy.

However, the issue is still to be decided; Lin did not indicate when she would release a final ruling.

Workday’s defense

A Workday spokesperson called the claims in the suit “false.”

“Workday’s AI recruiting tools don’t make hiring decisions and are designed with human oversight at their core,” the spokesperson told CIO. “Our technology looks only at job qualifications, not protected traits like race, age, or disability. We rigorously test our products as part of our responsible AI program to confirm our tools do not harm protected groups.”

Workday’s platform is meant to provide insights on how well a candidate’s qualifications match the requirements of a posted job, the company said. Those tools focus only on qualifications listed in a candidate’s application, which are compared to qualifications identified by the employer as important for the job.

Workday’s Chief Responsible AI Officer Kelly Trindel said its AI does not make employment decisions, automatically reject candidates, or determine who gets a job; further, she said, there is no evidence that the company’s tools result in harm to protected groups.

Trindel, who is former chief analyst of the Equal Employment Opportunity Commission (EEOC), leads a dedicated team composed of psychologists and PhD-level data scientists whose sole focus is to ensure that its AI is “responsible, fair, and ethical.” She said that the company’s AI systems undergo ongoing reviews throughout their lifecycle to help prevent unintended consequences, and Workday is “committed to accountability, transparency, and trust,” and invests “significant resources” into identifying and mitigating bias.

Further, she said, Workday has a company-wide commitment to ethical AI, and an independently-evaluated AI governance program based on standards from the National Institute of Standards and Technology (NIST) and the International Standards Organization (ISO).

“Workday builds AI to support people, not replace them, and this is of particular importance when it comes to hiring,” Trindel noted. Its platform is designed to help employers “manage high-volume processes more efficiently, surface relevant information, and reduce administrative work so teams can spend more time applying their expertise and judgement to hiring decisions.”

What this means for enterprise leaders

Workday isn’t alone in its legal challenges; other AI hiring tools are also being scrutinized over their methodologies, algorithms, and data-collecting practices. Eightfold, for one, is also facing a California class action lawsuit alleging that its tools unfairly rely on job candidates’ online data to predict whether they’d be a good fit for a position.

This means that enterprises, who are already feeling increased pressure to document hiring decisions, conduct AI bias audits, and maintain human oversight in recruitment and hiring, must be even more diligent in their vetting of AI tools.

Organizations must be actively defining how these recruitment tools should work, identifying bias in their algorithms, and setting up structures to test for bias across the tools’ decision-making logic, Info-Tech’s Howden advised.

“Validation of non-biased outcomes also needs to be active and ongoing, rather than a point-in-time exercise,” he said.

While Workday and others say human oversight is paramount, “it’s hard to incorporate humans into the process if the platform does the weeding out before humans have the ability to intervene,” Howden pointed out.

Discriminatory biases can exist in past hiring decisions, so it’s easy to forget that AI can “emulate and adapt those biases as part of its perspective,” he said. That includes how AI looks at language: Different cultures use different phrasing, and AI can capture that and use it to exclude candidates.

Ultimately, he called the case a “cautionary tale” illustrating how lightly some organizations have been treating AI risk. It also highlights the urgency involved in building out more advanced enterprise risk practices, “rather than relying on the limited capabilities they may have employed up until now.”

This article originally appeared on CIO.com.